Overview

Dataset statistics

Number of variables10
Number of observations442
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.7 KiB
Average record size in memory80.3 B

Variable types

Numeric9
Categorical1

Warnings

s1 is highly correlated with s2 and 2 other fieldsHigh correlation
s2 is highly correlated with s1 and 1 other fieldsHigh correlation
s3 is highly correlated with s4High correlation
s4 is highly correlated with s1 and 3 other fieldsHigh correlation
s5 is highly correlated with s1 and 1 other fieldsHigh correlation
s1 is highly correlated with s2 and 2 other fieldsHigh correlation
s2 is highly correlated with s1 and 1 other fieldsHigh correlation
s3 is highly correlated with s4High correlation
s4 is highly correlated with s1 and 3 other fieldsHigh correlation
s5 is highly correlated with s1 and 1 other fieldsHigh correlation
s1 is highly correlated with s2High correlation
s2 is highly correlated with s1High correlation
s3 is highly correlated with s4High correlation
s4 is highly correlated with s3High correlation
s4 is highly correlated with s5 and 3 other fieldsHigh correlation
s5 is highly correlated with s4 and 1 other fieldsHigh correlation
s3 is highly correlated with s4High correlation
s1 is highly correlated with s4 and 2 other fieldsHigh correlation
s2 is highly correlated with s4 and 1 other fieldsHigh correlation

Reproduction

Analysis started2021-08-28 11:48:35.180665
Analysis finished2021-08-28 11:48:45.167958
Duration9.99 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct58
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.63962254 × 10-16
Minimum-0.1072256316
Maximum0.1107266755
Zeros0
Zeros (%)0.0%
Negative202
Negative (%)45.7%
Memory size3.6 KiB
2021-08-28T17:48:45.266984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1072256316
5-th percentile-0.0854304009
Q1-0.03729926643
median0.005383060374
Q30.03807590643
95-th percentile0.07076875249
Maximum0.1107266755
Range0.2179523071
Interquartile range (IQR)0.07537517286

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)-1.308351267 × 1014
Kurtosis-0.6712236886
Mean-3.63962254 × 10-16
Median Absolute Deviation (MAD)0.03632538451
Skewness-0.231381533
Sum-1.608713163 × 10-13
Variance0.002267573696
MonotonicityNot monotonic
2021-08-28T17:48:45.372243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0162806757319
 
4.3%
0.0417084448817
 
3.8%
0.00901559882516
 
3.6%
-0.0273097856815
 
3.4%
-0.00188201652814
 
3.2%
-0.0527375548414
 
3.2%
0.0453409833414
 
3.2%
0.0126481372814
 
3.2%
0.0671362140413
 
2.9%
0.00538306037413
 
2.9%
Other values (48)293
66.3%
ValueCountFrequency (%)
-0.10722563163
 
0.7%
-0.10359309323
 
0.7%
-0.099960554712
 
0.5%
-0.096328016254
0.9%
-0.09269547784
0.9%
-0.089062939353
 
0.7%
-0.08543040095
1.1%
-0.081797862452
 
0.5%
-0.0781653244
0.9%
-0.074532785558
1.8%
ValueCountFrequency (%)
0.11072667552
 
0.5%
0.096196521652
 
0.5%
0.09256398321
 
0.2%
0.088931444751
 
0.2%
0.08529890631
 
0.2%
0.081666367855
 
1.1%
0.078033829391
 
0.2%
0.074401290946
1.4%
0.070768752497
1.6%
0.0671362140413
2.9%

sex
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
-0.044641636506989
235 
0.0506801187398187
207 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters7956
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0506801187398187
2nd row-0.044641636506989
3rd row0.0506801187398187
4th row-0.044641636506989
5th row-0.044641636506989

Common Values

ValueCountFrequency (%)
-0.044641636506989235
53.2%
0.0506801187398187207
46.8%

Length

2021-08-28T17:48:45.692868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-28T17:48:45.737230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.044641636506989235
53.2%
0.0506801187398187207
46.8%

Most occurring characters

ValueCountFrequency (%)
01533
19.3%
61147
14.4%
81063
13.4%
1856
10.8%
4705
8.9%
9677
8.5%
.442
 
5.6%
5442
 
5.6%
3442
 
5.6%
7414
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7279
91.5%
Other Punctuation442
 
5.6%
Dash Punctuation235
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01533
21.1%
61147
15.8%
81063
14.6%
1856
11.8%
4705
9.7%
9677
9.3%
5442
 
6.1%
3442
 
6.1%
7414
 
5.7%
Other Punctuation
ValueCountFrequency (%)
.442
100.0%
Dash Punctuation
ValueCountFrequency (%)
-235
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7956
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01533
19.3%
61147
14.4%
81063
13.4%
1856
10.8%
4705
8.9%
9677
8.5%
.442
 
5.6%
5442
 
5.6%
3442
 
5.6%
7414
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII7956
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01533
19.3%
61147
14.4%
81063
13.4%
1856
10.8%
4705
8.9%
9677
8.5%
.442
 
5.6%
5442
 
5.6%
3442
 
5.6%
7414
 
5.2%

bmi
Real number (ℝ)

Distinct163
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.013951493 × 10-16
Minimum-0.0902752959
Maximum0.170555226
Zeros0
Zeros (%)0.0%
Negative247
Negative (%)55.9%
Memory size3.6 KiB
2021-08-28T17:48:45.821871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0902752959
5-th percentile-0.06656343027
Q1-0.03422906806
median-0.00728376621
Q30.03124801543
95-th percentile0.08540807214
Maximum0.170555226
Range0.2608305219
Interquartile range (IQR)0.06547708349

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)-5.942018449 × 1013
Kurtosis0.09509447428
Mean-8.013951493 × 10-16
Median Absolute Deviation (MAD)0.03125655014
Skewness0.5981484879
Sum-3.54216656 × 10-13
Variance0.002267573696
MonotonicityNot monotonic
2021-08-28T17:48:45.957185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.024528759398
 
1.8%
-0.030995631848
 
1.8%
-0.0083615782847
 
1.6%
-0.046085000877
 
1.6%
-0.025606571477
 
1.6%
0.0013387303816
 
1.4%
0.0045721666036
 
1.4%
0.014272475276
 
1.4%
-0.02021751116
 
1.4%
-0.023450947326
 
1.4%
Other values (153)375
84.8%
ValueCountFrequency (%)
-0.09027529591
0.2%
-0.089197483821
0.2%
-0.084886235531
0.2%
-0.083808423461
0.2%
-0.081652799312
0.5%
-0.080574987231
0.2%
-0.079497175161
0.2%
-0.077341551012
0.5%
-0.076263738941
0.2%
-0.075185926861
0.2%
ValueCountFrequency (%)
0.1705552261
0.2%
0.16085491731
0.2%
0.13714305171
0.2%
0.12852055511
0.2%
0.1274427431
0.2%
0.12528711891
0.2%
0.12313149471
0.2%
0.11450899811
0.2%
0.11127556191
0.2%
0.11019774981
0.2%

bp
Real number (ℝ)

Distinct100
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.289817926 × 10-16
Minimum-0.1123996021
Maximum0.1320442172
Zeros0
Zeros (%)0.0%
Negative244
Negative (%)55.2%
Memory size3.6 KiB
2021-08-28T17:48:46.093690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1123996021
5-th percentile-0.07435588089
Q1-0.0366564468
median-0.005670610555
Q30.03564383777
95-th percentile0.08367188395
Maximum0.1320442172
Range0.2444438193
Interquartile range (IQR)0.07230028457

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)3.691920129 × 1014
Kurtosis-0.5327797228
Mean1.289817926 × 10-16
Median Absolute Deviation (MAD)0.03442870694
Skewness0.2906638512
Sum5.700995231 × 10-14
Variance0.002267573696
MonotonicityNot monotonic
2021-08-28T17:48:46.211796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0400993174921
 
4.8%
-0.00567061055521
 
4.8%
-0.0263278347220
 
4.5%
0.0218723549915
 
3.4%
-0.033213576114
 
3.2%
-0.0228849640213
 
2.9%
-0.0159992226411
 
2.5%
0.0081008722211
 
2.5%
-0.0125563519411
 
2.5%
0.0494153205411
 
2.5%
Other values (90)294
66.5%
ValueCountFrequency (%)
-0.11239960211
 
0.2%
-0.10895673141
 
0.2%
-0.102070991
 
0.2%
-0.10092336641
 
0.2%
-0.098628119291
 
0.2%
-0.084856636514
0.9%
-0.081413765824
0.9%
-0.077970895121
 
0.2%
-0.074528024439
2.0%
-0.071085153741
 
0.2%
ValueCountFrequency (%)
0.13204421721
 
0.2%
0.12515847581
 
0.2%
0.10794412233
0.7%
0.10450125162
 
0.5%
0.1010583811
 
0.2%
0.098763133711
 
0.2%
0.097615510265
1.1%
0.094172639561
 
0.2%
0.090729768872
 
0.5%
0.087286898184
0.9%

s1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct141
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.042540472 × 10-17
Minimum-0.1267806699
Maximum0.1539137132
Zeros0
Zeros (%)0.0%
Negative240
Negative (%)54.3%
Memory size3.6 KiB
2021-08-28T17:48:46.330992image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1267806699
5-th percentile-0.07311850845
Q1-0.0342478402
median-0.004320865537
Q30.02835801485
95-th percentile0.08367131975
Maximum0.1539137132
Range0.2806943831
Interquartile range (IQR)0.06260585505

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)-5.26611385 × 1014
Kurtosis0.2329479047
Mean-9.042540472 × 10-17
Median Absolute Deviation (MAD)0.03095893931
Skewness0.3781082069
Sum-3.996802889 × 10-14
Variance0.002267573696
MonotonicityNot monotonic
2021-08-28T17:48:46.445258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.00707277125310
 
2.3%
-0.0373437341310
 
2.3%
0.012190568769
 
2.0%
0.020446285919
 
2.0%
0.0011829458968
 
1.8%
0.024574144498
 
1.8%
-0.024960158418
 
1.8%
-0.0043208655378
 
1.8%
-0.0029449126788
 
1.8%
-0.0098246769697
 
1.6%
Other values (131)357
80.8%
ValueCountFrequency (%)
-0.12678066991
0.2%
-0.10889328281
0.2%
-0.10476542421
0.2%
-0.10338947131
0.2%
-0.10063756561
0.2%
-0.096509707042
0.5%
-0.09100589561
0.2%
-0.089629942752
0.5%
-0.088253989891
0.2%
-0.086878037031
0.2%
ValueCountFrequency (%)
0.15391371321
0.2%
0.15253776031
0.2%
0.13327442031
0.2%
0.12777060892
0.5%
0.1263946561
0.2%
0.12501870312
0.5%
0.11951489171
0.2%
0.10988322172
0.5%
0.10300345741
0.2%
0.098875598831
0.2%

s2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct302
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.301121101 × 10-16
Minimum-0.115613066
Maximum0.1987879897
Zeros0
Zeros (%)0.0%
Negative239
Negative (%)54.1%
Memory size3.6 KiB
2021-08-28T17:48:46.576105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.115613066
5-th percentile-0.07271172671
Q1-0.03035839726
median-0.003819065121
Q30.02984439452
95-th percentile0.07946276829
Maximum0.1987879897
Range0.3144010556
Interquartile range (IQR)0.06020279178

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)3.659847463 × 1014
Kurtosis0.6013811504
Mean1.301121101 × 10-16
Median Absolute Deviation (MAD)0.0299056781
Skewness0.4365918037
Sum5.750955268 × 10-14
Variance0.002267573696
MonotonicityNot monotonic
2021-08-28T17:48:46.707822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0010007289645
 
1.1%
0.016222436435
 
1.1%
0.0566185884
 
0.9%
-0.024800012064
 
0.9%
-0.047033552854
 
0.9%
-0.01383981594
 
0.9%
-0.054549115933
 
0.7%
-0.021668527443
 
0.7%
0.0046359433483
 
0.7%
0.037516531843
 
0.7%
Other values (292)404
91.4%
ValueCountFrequency (%)
-0.1156130661
0.2%
-0.11279472981
0.2%
-0.1068449091
0.2%
-0.10433972141
0.2%
-0.10089508831
0.2%
-0.097137306731
0.2%
-0.096197861351
0.2%
-0.095884712891
0.2%
-0.094632119041
0.2%
-0.090561189041
0.2%
ValueCountFrequency (%)
0.19878798971
0.2%
0.15588665041
0.2%
0.13146107041
0.2%
0.13020847651
0.2%
0.12801643731
0.2%
0.12739014041
0.2%
0.12519810111
0.2%
0.11705624111
0.2%
0.11642994421
0.2%
0.10891438111
0.2%

s3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct63
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.563971122 × 10-16
Minimum-0.1023070505
Maximum0.1811790604
Zeros0
Zeros (%)0.0%
Negative243
Negative (%)55.0%
Memory size3.6 KiB
2021-08-28T17:48:46.830910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1023070505
5-th percentile-0.06549067248
Q1-0.03511716059
median-0.006584467611
Q30.02931150098
95-th percentile0.07790911999
Maximum0.1811790604
Range0.2834861109
Interquartile range (IQR)0.06442866157

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)-1.043368732 × 1014
Kurtosis0.9815074614
Mean-4.563971122 × 10-16
Median Absolute Deviation (MAD)0.03129392133
Skewness0.7992551183
Sum-2.017275236 × 10-13
Variance0.002267573696
MonotonicityNot monotonic
2021-08-28T17:48:46.946643image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0139477432222
 
5.0%
-0.0434008456519
 
4.3%
-0.0397192078518
 
4.1%
-0.00290282980715
 
3.4%
-0.0323559322415
 
3.4%
-0.0213110188315
 
3.4%
0.00814208360515
 
3.4%
-0.0286742944415
 
3.4%
-0.00658446761114
 
3.2%
0.0155053592114
 
3.2%
Other values (53)280
63.3%
ValueCountFrequency (%)
-0.10230705051
 
0.2%
-0.098625412711
 
0.2%
-0.091262137111
 
0.2%
-0.080217223692
 
0.5%
-0.076535585895
1.1%
-0.072853948085
1.1%
-0.069172310287
1.6%
-0.065490672486
1.4%
-0.061809034677
1.6%
-0.058127396878
1.8%
ValueCountFrequency (%)
0.18117906041
 
0.2%
0.17749742261
 
0.2%
0.17381578481
 
0.2%
0.15908923361
 
0.2%
0.1517259581
 
0.2%
0.14068104461
 
0.2%
0.13331776891
 
0.2%
0.12227285552
0.5%
0.11859121773
0.7%
0.10386466651
 
0.2%

s4
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct66
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.863174235 × 10-16
Minimum-0.07639450375
Maximum0.1852344433
Zeros0
Zeros (%)0.0%
Negative288
Negative (%)65.2%
Memory size3.6 KiB
2021-08-28T17:48:47.060733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.07639450375
5-th percentile-0.07639450375
Q1-0.03949338287
median-0.002592261998
Q30.03430885888
95-th percentile0.08076737006
Maximum0.1852344433
Range0.261628947
Interquartile range (IQR)0.07380224175

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)1.232640433 × 1014
Kurtosis0.4444016718
Mean3.863174235 × 10-16
Median Absolute Deviation (MAD)0.03690112088
Skewness0.7353736479
Sum1.707523012 × 10-13
Variance0.002267573696
MonotonicityNot monotonic
2021-08-28T17:48:47.181525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.03949338287128
29.0%
-0.002592261998108
24.4%
0.0343088588868
15.4%
0.0712099797533
 
7.5%
-0.0763945037528
 
6.3%
0.108111100613
 
2.9%
0.14501222152
 
0.5%
-0.037648326832
 
0.5%
0.015858298442
 
0.5%
-0.021411833642
 
0.5%
Other values (56)56
12.7%
ValueCountFrequency (%)
-0.0763945037528
 
6.3%
-0.070859335621
 
0.2%
-0.069383290781
 
0.2%
-0.053515808811
 
0.2%
-0.051670752761
 
0.2%
-0.050563719141
 
0.2%
-0.050194707931
 
0.2%
-0.047980640681
 
0.2%
-0.047242618261
 
0.2%
-0.03949338287128
29.0%
ValueCountFrequency (%)
0.18523444331
 
0.2%
0.15534453541
 
0.2%
0.14501222152
 
0.5%
0.14132210941
 
0.2%
0.13025177321
 
0.2%
0.108111100613
2.9%
0.091874607441
 
0.2%
0.086708450521
 
0.2%
0.084863394481
 
0.2%
0.080804271181
 
0.2%

s5
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct184
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.848103334 × 10-16
Minimum-0.1260973856
Maximum0.13359898
Zeros0
Zeros (%)0.0%
Negative230
Negative (%)52.0%
Memory size3.6 KiB
2021-08-28T17:48:47.292938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1260973856
5-th percentile-0.0721284546
Q1-0.03324878725
median-0.001947634157
Q30.03243322578
95-th percentile0.07904666678
Maximum0.13359898
Range0.2596963656
Interquartile range (IQR)0.06568201303

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)-1.237468007 × 1014
Kurtosis-0.1343658334
Mean-3.848103334 × 10-16
Median Absolute Deviation (MAD)0.03314062486
Skewness0.2917738324
Sum-1.700861674 × 10-13
Variance0.002267573696
MonotonicityNot monotonic
2021-08-28T17:48:47.443385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0181182673111
 
2.5%
-0.0307512098610
 
2.3%
-0.041180385198
 
1.8%
-0.051400535267
 
1.6%
-0.025952424447
 
1.6%
-0.033248787257
 
1.6%
-0.010904435856
 
1.4%
-0.00060925418616
 
1.4%
-0.061176595096
 
1.4%
-0.023644557576
 
1.4%
Other values (174)368
83.3%
ValueCountFrequency (%)
-0.12609738561
 
0.2%
-0.10436482081
 
0.2%
-0.10164354791
 
0.2%
-0.096433222894
0.9%
-0.093935645511
 
0.2%
-0.089136860081
 
0.2%
-0.086828993222
0.5%
-0.082381483262
0.5%
-0.080236540251
 
0.2%
-0.078140910672
0.5%
ValueCountFrequency (%)
0.133598982
0.5%
0.13339573381
0.2%
0.13237264931
0.2%
0.13008060951
0.2%
0.12901941161
0.2%
0.1200533821
0.2%
0.11934399421
0.2%
0.10635427671
0.2%
0.10413761141
0.2%
0.10329226491
0.2%

s6
Real number (ℝ)

Distinct56
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.398488127 × 10-16
Minimum-0.1377672257
Maximum0.1356118307
Zeros0
Zeros (%)0.0%
Negative224
Negative (%)50.7%
Memory size3.6 KiB
2021-08-28T17:48:47.564477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1377672257
5-th percentile-0.07563562197
Q1-0.03317902609
median-0.0010776975
Q30.0279170509
95-th percentile0.0817644408
Maximum0.1356118307
Range0.2733790564
Interquartile range (IQR)0.06109607699

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)-1.401183286 × 1014
Kurtosis0.2369167379
Mean-3.398488127 × 10-16
Median Absolute Deviation (MAD)0.0289947484
Skewness0.2079166162
Sum-1.502131752 × 10-13
Variance0.002267573696
MonotonicityNot monotonic
2021-08-28T17:48:47.685581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00306440941422
 
5.0%
0.0196328370720
 
4.5%
0.00720651632920
 
4.5%
-0.001077697519
 
4.3%
-0.0135040182416
 
3.6%
-0.0176461251616
 
3.6%
-0.0383566597315
 
3.4%
-0.0549250873914
 
3.2%
-0.00521980441514
 
3.2%
0.0154907301614
 
3.2%
Other values (46)272
61.5%
ValueCountFrequency (%)
-0.13776722571
 
0.2%
-0.12948301192
 
0.5%
-0.10463037042
 
0.5%
-0.096346156542
 
0.5%
-0.092204049634
0.9%
-0.088061942712
 
0.5%
-0.08391983583
0.7%
-0.079777728884
0.9%
-0.075635621974
0.9%
-0.071493515055
1.1%
ValueCountFrequency (%)
0.13561183073
0.7%
0.13146972382
0.5%
0.12732761691
 
0.2%
0.1190434032
0.5%
0.10661708234
0.9%
0.098332868462
0.5%
0.094190761541
 
0.2%
0.090048654632
0.5%
0.085906547714
0.9%
0.08176444084
0.9%

Interactions

2021-08-28T17:48:37.456521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:37.543148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:37.627273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:37.724538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:37.811399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:37.896512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:37.988476image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:38.070463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:38.151913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:38.243573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:38.335454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:38.424949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:38.508694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:38.600534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:38.692365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:38.782315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:38.870085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:38.955078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:39.046778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:39.132705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:39.218205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:39.301240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:39.382863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:39.554958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:39.638864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:39.720491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:39.808563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:39.895334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:39.982074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:40.075795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:40.168577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:40.263328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:40.360447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:40.452093image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:40.535662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:40.627336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:40.719420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:40.817761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:40.911510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:41.001274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:41.092257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:41.192314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:41.285584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:41.377816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:41.467428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:41.569679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:41.661434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:41.755183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:41.846943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:41.936981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:42.029732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:42.120185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:42.305264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:42.396904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:42.488703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:42.570341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:42.662365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:42.744018image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:42.834376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:42.923144image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:43.009907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:43.093687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:43.175551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:43.257798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:43.347519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:43.430929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:43.513616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:43.603567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:43.695410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:43.785627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:43.868848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:43.960259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:44.041749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:44.133505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:44.223192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:44.306706image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:44.398552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:44.491171image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:44.572813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:44.664647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-28T17:48:44.746265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-08-28T17:48:47.795513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-28T17:48:47.937166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-28T17:48:48.076903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-28T17:48:48.218509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-08-28T17:48:44.924727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-28T17:48:45.087627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

agesexbmibps1s2s3s4s5s6
00.0380760.0506800.0616960.021872-0.044223-0.034821-0.043401-0.0025920.019908-0.017646
1-0.001882-0.044642-0.051474-0.026328-0.008449-0.0191630.074412-0.039493-0.068330-0.092204
20.0852990.0506800.044451-0.005671-0.045599-0.034194-0.032356-0.0025920.002864-0.025930
3-0.089063-0.044642-0.011595-0.0366560.0121910.024991-0.0360380.0343090.022692-0.009362
40.005383-0.044642-0.0363850.0218720.0039350.0155960.008142-0.002592-0.031991-0.046641
5-0.092695-0.044642-0.040696-0.019442-0.068991-0.0792880.041277-0.076395-0.041180-0.096346
6-0.0454720.050680-0.047163-0.015999-0.040096-0.0248000.000779-0.039493-0.062913-0.038357
70.0635040.050680-0.0018950.0666300.0906200.1089140.0228690.017703-0.0358170.003064
80.0417080.0506800.061696-0.040099-0.0139530.006202-0.028674-0.002592-0.0149560.011349
9-0.070900-0.0446420.039062-0.033214-0.012577-0.034508-0.024993-0.0025920.067736-0.013504

Last rows

agesexbmibps1s2s3s4s5s6
4320.009016-0.0446420.055229-0.0056710.0575970.044719-0.0029030.0232390.0556840.106617
433-0.027310-0.044642-0.060097-0.0297710.0465890.0199800.122273-0.039493-0.051401-0.009362
4340.016281-0.0446420.0013390.0081010.0053110.0108990.030232-0.039493-0.0454210.032059
435-0.012780-0.044642-0.023451-0.040099-0.0167040.004636-0.017629-0.002592-0.038459-0.038357
436-0.056370-0.044642-0.074108-0.050428-0.024960-0.0470340.092820-0.076395-0.061177-0.046641
4370.0417080.0506800.0196620.059744-0.005697-0.002566-0.028674-0.0025920.0311930.007207
438-0.0055150.050680-0.015906-0.0676420.0493410.079165-0.0286740.034309-0.0181180.044485
4390.0417080.050680-0.0159060.017282-0.037344-0.013840-0.024993-0.011080-0.0468790.015491
440-0.045472-0.0446420.0390620.0012150.0163180.015283-0.0286740.0265600.044528-0.025930
441-0.045472-0.044642-0.073030-0.0814140.0837400.0278090.173816-0.039493-0.0042200.003064